Occupancy Detection – Office Room Environmental Sensors Dataset
Abstract
"Experimental office-room dataset for binary occupancy detection using indoor temperature, relative humidity, light level, CO₂ concentration, and humidity ratio at one-minute intervals.[web:199]"
Description
Overview
The Occupancy Detection dataset contains experimental data from an office room instrumented with environmental and CO₂ sensors, created to evaluate statistical learning models for detecting whether the room is occupied.[web:199]
Data Collection
- Three files (training and two test sets) with a total of 20,560 time-stamped instances sampled every minute.[web:199]
- Ground-truth occupancy (0 or 1) was obtained from time-stamped pictures taken every minute and manually labeled as occupied or not occupied.[web:199]
Variables
- date and time fields plus an integer id for each observation.[web:199]
- Temperature (°C), Humidity (%), Light (Lux), CO₂ (ppm), and HumidityRatio (kg water-vapor/kg air).[web:199]
- Occupancy: binary target label, 0 for not occupied and 1 for occupied.[web:199]
Use Cases
- Developing occupancy detection models for smart HVAC and building automation.[web:199]
- Evaluating classification algorithms on realistic indoor environmental sensor data.[web:199]
License
The UCI page specifies that this dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0), which permits sharing and adaptation with appropriate credit.[web:199]
📊 View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on UCI Machine Learning Repository.
Preview on UCI Machine Learning Repository
Cite This Dataset
Candanedo, Luis (2016). Occupancy Detection – Office Room Environmental Sensors Dataset. [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5X01N
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Original source: UCI Machine Learning Repository (2016). Visit official page for more details.
Indexed by IoTDataset.com on Jan 28, 2026
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